Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties Finally, this paper overviews the current standardization activities and elaborates further on open research challenges

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On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.

A Survey on the Edge Computing for the Internet of Things

As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures, which bring network functions and contents to the network edge, are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks, including definition, architecture, and advantages. Next, a comprehensive survey of issues on computing, caching, and communication techniques at the network edge is presented. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks, such as cloud technology, SDN/NFV, and smart devices are discussed. Finally, open research challenges and future directions are presented as well.

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

Mobile-Edge computing (MEC) is an emerging technology currently recognized as a key enabler for 5G networks. Compatible with current 4G networks, MEC will address many key uses of the 5G system, motivated by the massive diffusion of the Internet of Things (IoT). This article aims to provide a tutorial on MEC technology and an overview of the MEC framework and architecture recently defined by the European Telecommunications Standards Institute (ETSI) MEC Industry Specification Group (ISG) standardization organization. We provide some examples of MEC deployment, with special reference to IoT cases, since IoT is recognized as a main driver for 5G. Finally, we discuss the main benefits and challenges for MEC moving toward 5G.

Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things.

A method is described for operating a mobile station with a wireless network having packet data capabilities, as is a network that operates in accordance with the method. The method includes establishing default operational parameters values for the mobile station and, when establishing a PDP Context for use by an application, such as a delay-critical application that may be a real-time application (or any application that requires specific operational values, such as DRX values), requesting a modification of the default operational parameters values so as to reduce the delay in the establishment of a TBF for the application. In the preferred embodiment the operational parameters comprise DRX parameters selected to optimize the paging operation of the MS to reduce the delay in transferring packet data to or from the MS. In other embodiments the operational parameters can include MS Radio Access Capability (RAC) parameters, such as Multi-slot Class, GPRS Timers and/or MS Power Class. In one embodiment, requesting a modification includes sending a PDP Context-related message from the mobile station to the network with an information element that specifies values for parameters of the DRX mode, where the values comprise a Split_PG_Cycle code and a non-DRX timer. In this case the PDP Context-related messages comprise one of an Activate PDP Context request, an Activate Secondary PDP Context request and a Modify PDP Context request. The method further includes, when terminating the PDP Context, requesting a modification of the paging parameters values so as to have the default or other parameters values. Preferably, when terminating the PDP Context the mobile station sends a Deactivate PDP Context request message with the information element that specifies the default values for the parameters of the DRX mode.

Enhanced PDP context management using radio parameter information elements added to messages

A data transmission method transmits data to a group of receiving communication devices in a communication system comprising at least one cellular network The method includes defining, by an application server situated beyond a cellular network, a group of receiving communication devices in the cellular network, the application server having no visibility to locations of receiving devices in the cellular network The method also includes receiving, in a broadcast/multicast network entity configured to provide broadcast and multicast services in the cellular network, data to be transmitted from the application server to the group The method also includes transmitting the data from the broadcast/multicast network entity to the group using broadcast and/or multicast transmission Furthermore, a network entity in a cellular network and a communication system are provided configured to implement the method

Transmitting data to a group of receiving devices

A controller for controlling a multiparty session in a communication system and a method for is disclosed. The controller comprises a control function configured to host a multiparty session. The control function controls joining of parties to the multiparty session and also selects at least one communication parameter based on discovered capabilities of the parties to the multiparty session. After the selection, information regarding the selected at least one communication parameter is sent to at least one party of the multiparty session.

Multi-party sessions in a communication system

A method enables obtaining parameters relating to a service provided in a communication system. The method comprises storing a first set of parameters relating to a service, the first set of parameters being valid when the service is controlled by a first network. The method further comprises determining whether a second set of parameters is needed, the second set of parameters relating to said service and being valid when said service is controlled by a second network. The method further comprises obtaining the second set of parameters, when needed. A communication device is configured to execute the method.

Pekka Kuure

Papers

Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things.

Enhanced PDP context management using radio parameter information elements added to messages

Transmitting data to a group of receiving devices

Multi-party sessions in a communication system

Service provision in a communication system